PHOTOMETRIC MONITORING OF THE COLDEST KNOWN BROWN DWARF WITH THE SPITZER SPACE TELESCOPE^*

In multiple temperature regimes for brown dwarfs, condensates are predicted to form clouds, which can significantly influence the emergent spectra and colors (Ackerman & Marley 2001). The spectra of L dwarfs (1300–2200 K; Stephens et al. 2009) are best fit by models that include a thick cloud layer of iron, silicates, and corundum (Saumon & Marley 2008). Those clouds break up nonuniformly and disappear as brown dwarfs grow cooler and enter the T dwarf sequence (500–1300 K; Stephens et al. 2009), as indicated by the near-infrared (IR) colors (Burgasser et al. 2002), photometric and spectral variability (Buenzli et al. 2014; Burgasser et al. 2014; Radigan 2014; Radigan et al. 2014; Wilson et al. 2014; Yang et al. 2016), and surface maps (Crossfield et al. 2014; Karalidi et al. 2016) of objects near the L/T transition. Clouds may appear again below 900 K based on the colors of late T dwarfs, this time in the form of sulfides (Morley et al. 2012). Photometric variability at near-IR wavelengths has been reported in this temperature regime, which has been attributed to clouds (Yang et al. 2016). Among the Y dwarfs (<500 K; Dupuy & Kraus 2013), additional clouds of water and ammonia are predicted to form at <350 K and <200 K, respectively (Burrows et al. 2003; Morley et al. 2014a). When water clouds are present, they are expected to be patchy (Morley et al. 2014a) and hence amenable to detection through variability. The only Y dwarfs with published time-series photometry, WISE J140518.39+553421.3 (hereafter WISE 1405+5534) and WISEP J173835.52+273258.9, do exhibit variability, but they are likely too warm to have water ice clouds (∼400 K; Cushing et al. 2016; Leggett et al. 2016).

The most promising brown dwarf for the detection of water clouds is WISE J085510.83–071442.5 (hereafter WISE 0855-0714). It is the coldest known brown dwarf (∼250 K; Luhman 2014), making it the most likely one to harbor water clouds. In addition, it is the fourth closest system to the Sun (2.23 ± 0.04 pc; Luhman 2014; Luhman & Esplin 2016), so it is relatively bright for its low luminosity. As with other Y dwarfs⁵ , WISE 0855-0714 is much too faint at near-IR wavelengths for accurate photometric monitoring (Beamín et al. 2014; Faherty et al. 2014; Kopytova et al. 2014; Luhman 2014; Wright et al. 2014; Luhman & Esplin 2016; Schneider et al. 2016). Currently, such measurements are only feasible in mid-IR bands with the Infrared Array Camera (IRAC; Fazio et al. 2004) on the Spitzer Space Telescope (Werner et al. 2004).

In this paper, we present time-series IRAC photometry of WISE 0855-0714 during two 23 hr periods. We begin by describing the observations and data reduction (Section 2). We use these data to characterize the variability of WISE 0855-0714, which is then compared to the predictions of models that produce variability through either patchy clouds or hot spots (Section 3). We conclude by assessing the evidence of water ice clouds in the atmosphere of WISE 0855-0714 from our variability measurements and previous observations (Section 4).

In the postcryogenic mission of Spitzer, IRAC collects data with two operable 256 × 256 arrays. Each array has a plate scale of 12 pixel⁻¹ and a field of view of 52 × 52. The arrays simultaneously image adjacent areas of the sky in filters centered at 3.6 and 4.5 μm, which are denoted as [3.6] and [4.5], respectively. Point sources in the images have FWHM = 17.

To minimize the errors in our time-series IRAC photometry of WISE 0855-0714 due to variations in intrapixel sensitivity (Reach et al. 2005), the images were taken in the "staring mode" with the following strategy (Krick et al. 2015): (1) for each of the two filters, the target was placed on the "sweet spot" for that array, which is a portion of a pixel near the corner of the array in which the sensitivity as a function of intrapixel position has been well characterized and (2) prior to collection of the science data, the target was imaged for 30 minutes at [4.5] to provide time for the spacecraft pointing to settle. Following those steps, WISE 0855-0714 was observed continuously during two 23 hr periods on 2015 March 10 and 2015 August 3. During each period, we obtained 405 images with exposure times of 96.8 s at [4.5], which were immediately followed by 405 images with exposure times of 93.6 s at [3.6]. These data were collected through Astronomical Observation Requests 52667904, 52668160, 52668672, and 52668928 within program 11056 (K. Luhman).

We began our reduction of the data using the Corrected Basic Calibrated Data frames produced by the pipeline at the Spitzer Science Center (SSC). We measured positions and fluxes of WISE 0855-0714 in each of those frames with a point response function (PRF) fitting routine in the SSC's Astronomical Point source EXtractor (APEX; Makovoz & Marleau 2005), which produces more accurate IRAC astrometry than other commonly used algorithms (Esplin & Luhman 2016). APEX was used with the default parameters except for a 5 × 5 pixel fitting region. Because aperture photometry has been measured for most previous time-series data from IRAC, we also applied that method to our data for comparison to the results of PRF fitting. The aperture photometry was measured with phot in IRAF using an aperture radius of 1.5 pixels and a background annulus of 3 pixels, which was found to produce the least scatter in photometry for WISE 0855-0714 relative to other annuli. To compare the data from PRF fitting and aperture photometry in a given band, we calculated the median absolute deviation (MAD) of the data and rejected outliers that deviated by >3× MAD. We then fit the unrejected data using a Nadaraya–Watson regression estimator as implemented in the function npregbw from the np package (Hayfield & Racine 2008) within R (R Core Team 2013). After dividing the data by that fit to remove intrinsic variability, we recomputed the MAD. The MADs from APEX and phot were similar for [4.5], but APEX produced significantly lower values at [3.6], where WISE 0855-0714 is much fainter.

We have investigated methods of correcting for systematic noise in our photometry due to the varying intrapixel sensitivities. The SSC has measured high-resolution gain maps of the sweet spots for [3.6] and [4.5], which can be used for such corrections. However, because WISE 0855-0714 is much fainter at [3.6] than [4.5], the uncertainties in its positions in individual frames are larger than the sweet spot of [3.6], and thus a correction for varying intrapixel sensitivities was not possible in that band. At [4.5], the positions of WISE 0855-0714 drifted out of the sweet spot for roughly half of the first epoch, but they remained within it throughout the second epoch. As a result, the latter data were suitable for correction using the SSC's gain map. Applying that correction required that we reduce those data a second time following the SSC's recommended procedure, which is performed with the IDL routines box_centroider, aper, and pixel_phase_correct_gauss (Krick et al. 2015). The application of the gain map predicted a maximum change of <0.5% and did not produce a lower MAD or any noticeable change to the intrinsic variability compared to the uncorrected photometry from APEX. We tested an alternative method of correcting for the varying intrapixel sensitivity from Knutson et al. (2008) and Heinze et al. (2013), but it also did not reduce the MADs for either of the two epochs at [4.5] or alter the variability behavior. Therefore, we have adopted the uncorrected photometry produced by PRF fitting with APEX for both bands and epochs. Typical signal-to-noise ratios (S/N) for those data are 10.2/146.8 and 10.9/142.3 for [3.6]/[4.5] in the first and second epochs, respectively. The APEX data are listed in Tables 1 and 2 and are plotted as a function of time in Figure 1. We have omitted from Figure 1 measurements that deviated by more than 3 × MAD from the Nadaraya–Watson regression that we calculated previously.

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Because IRAC is capable of measuring astrometry with high precision (Esplin & Luhman 2016), it might seem possible to search for perturbations in the astrometry of WISE 0855-0714 due to an unseen companion. However, for any plausible combination of orbital separation and mass ratio, the astrometric perturbations would be too small to detect during a 12 hr period given the typical astrometric errors of 7 mas for WISE 0855-0714 in individual [4.5] frames and additional systematic errors among those frames.

3.1. Variability Characteristics

3.2. Patchy Water Clouds

If water clouds are present in the atmospheres of brown dwarfs as cold as WISE 0855-0714, those clouds are likely to be patchy rather than uniform (Morley et al. 2014a). To construct a self-consistent and stable model with nonuniform cloud coverage, Morley et al. (2014a) computed emergent spectra for cloudy and clear atmospheric columns with a single temperature–pressure profile and then combined the fluxes (F_ν) from the two columns in the following manner:

$$\begin{eqnarray}&&{F}_{\nu ,\mathrm{total}}={{hF}}_{\nu ,\mathrm{clear}}+(1-h){F}_{\nu ,\mathrm{cloudy}},\end{eqnarray} \tag{ 1 }$$

where h is the fraction of the atmosphere without clouds (Marley et al. 2010). Any deviation of h between hemispheres from the global average of a model for WISE 0855-0714 would produce photometric variability. In addition, these patchy cloud models predict substantially different emergent flux between the clear and cloudy columns at the two IRAC bands. For example, in Figure 2 we show the spectra of both columns for a model brown dwarf with T_eff = 250 K, log g = 4.0 cm s⁻², f_sed⁶  = 3, and a cloud coverage of 50% (h = 0.5) (Morley et al. 2014a).⁷ The cloudy column exhibits much lower flux longward of ∼3 μm, while the near-IR spectrum is affected less by the presence of water clouds. As a result, our IRAC observations are ideal for detecting photometric variability produced from water clouds.

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To test whether water clouds can produce the observed variability in WISE 0855-0714, we follow the prescription of Cushing et al. (2006) and Cushing et al. (2016). Using the h = 0.5 model of Figure 2, we assumed that the cloud cover between hemispheres deviates from the global average by Δh and calculated predicted fluxes for ${F}_{\nu ,h+{\rm{\Delta }}h}$ and ${F}_{\nu ,h-{\rm{\Delta }}h}$ from Equation (1). This deviation would produce variability with a semiamplitude of

$$\begin{eqnarray}&&{A}_{\lambda }=\displaystyle \frac{\max [{F}_{\nu ,h+{\rm{\Delta }}h}(\lambda ),{F}_{\nu ,h-{\rm{\Delta }}h}(\lambda )]}{\mathrm{average}[{F}_{\nu ,h+{\rm{\Delta }}h}(\lambda ),{F}_{\nu ,h-{\rm{\Delta }}h}(\lambda )]}-1.\end{eqnarray} \tag{ 2 }$$

In Figure 3, we show the predicted semiamplitudes at [3.6] and [4.5] as a function of Δh for T_eff = 250 K, log g = 4.0 cm s⁻², cloud coverage of 50%, and f_sed = 3, 5, and 7. Amplitudes in the two bands do not differ significantly for Δh < 0.05 and increase linearly with increasing deviation from homogeneous cloud cover. We find that only a small deviation from a global average h (Δh ≈ 0.01) is needed to reproduce the observed amplitudes of variation in both bands for WISE 0855-0714. Although this model of patchy clouds is able to reproduce the observed variability of WISE 0855-0714, it may not be the best explanation of those data, as discussed in Section 4.

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3.3. Hot Spots

Previous studies have attempted to constrain the presence of water ice clouds in the atmosphere of WISE 0855-0714 using photometry and spectroscopy. Faherty et al. (2014) reported a possible 2.6σ detection of WISE 0855-0714 in a medium-band filter within the J band. Those data were used to place the object in a diagram of M_W2 versus $J-W2$, where W2 is a band from the Wide-field Infrared Survey Explorer (Wright et al. 2010) that is similar to [4.5] from Spitzer. The position of WISE 0855-0714 in that diagram was better reproduced by cloudy models than cloudless models (Morley et al. 2012, 2014a; Saumon et al. 2012), which was interpreted as evidence of water ice clouds. However, Luhman & Esplin (2014) demonstrated that WISE 0855-0714 was roughly midway between those cloudless and cloudy models in a similar diagram of M_4.5 versus J − [4.5], and that its position was best matched by cloudless models that employed nonequilibrium chemistry (Saumon & Marley 2008; Saumon et al. 2012). After measuring photometry for WISE 0855-0714 in several additional near-IR bands, Schneider et al. (2016) and Luhman & Esplin (2016) found that no single suite of models provided a clearly superior match to the observed spectral energy distribution (SED), and that all of the models differed significantly from the data. Thus, the photometry and models that are currently available do not provide any indication of whether water ice clouds are present in WISE 0855-0714.

A spectroscopic investigation of water ice clouds in WISE 0855-0714 has been recently performed by Skemer et al. (2016). They obtained the only spectrum to date of the brown dwarf, which spans from 4.5 to 5.1 μm. Although its resolution and S/N were low, the spectrum exhibited absorption features that appeared to be statistically significant, many of which coincided with features in model spectra computed by Skemer et al. (2016). The spectra predicted by cloudless and partly cloudy models were indistinguishable for the wavelength range and resolution of the data. The atmospheric temperature structure for a brown dwarf near the temperature of WISE 0855-0714 does not converge with full coverage of water clouds (Morley et al. 2014a), so Skemer et al. (2016) developed a simplified model for that scenario, which used a gray, fully absorbing cloud with no specified composition. The cloud-top pressure in that model was varied to optimize the match to the observed spectrum. The resulting best-fit spectrum agreed somewhat better with the data than the spectra from the cloudless and partly cloudy models, which was cited as a detection of clouds. Given the temperature of WISE 0855-0714, it is expected that such clouds would be composed of water (Burrows et al. 2003; Morley et al. 2014a).

It is unclear whether the spectrum from Skemer et al. (2016) actually contains evidence of water clouds. The fully cloudy model from that study considered gray absorbers, whereas water ice is nongray across the wavelength range of the spectrum of WISE 0855-0714 (Morley et al. 2014a), and it assumed that the cloud coverage is uniform, which is not expected for water clouds (Morley et al. 2014a). In addition, models with patchy clouds have self-consistent temperature structures, which has not been possible for the fully cloudy models (Morley et al. 2014a). Given the variety of uncertainties in models of the coldest brown dwarfs (Morley et al. 2014a) and the large differences between the observed and predicted SEDs for WISE 0855-0714 (Luhman & Esplin 2016; Schneider et al. 2016), the current models may not be sufficiently accurate for subtle differences in predicted spectra to provide meaningful insight into the physical properties of WISE 0855-0714.

Our variability measurements can provide additional constraints on the existence of water ice clouds in the atmosphere of WISE 0855-0714. According to the partly cloudy models of Morley et al. (2014b), cloud-induced variability at mid-IR wavelengths should become much larger (for a given cloud covering fraction) at temperatures below ∼375 K with the onset of water clouds. The amplitudes that we have measured for WISE 0855-0714 (∼250 K) are only a few percent, which would require very small deviations in cloud coverage between hemispheres (Δh ∼ 0.01) if water clouds are responsible for the variability. Meanwhile, the mid-IR amplitudes for WISE 0855-0714 are similar to those observed for early Y dwarfs (∼400 K, Cushing et al. 2016; Leggett et al. 2016) and late T dwarfs (800–1000 K, Metchev et al. 2015; Yang et al. 2016), which should be too warm to harbor water clouds. If water clouds are producing the variability of WISE 0855-0714, then it must coincidentally have a value of Δh that produces roughly the same amplitudes as the different variability mechanisms that operate in those objects. Alternatively, the similarity in the amplitudes of WISE 0855-0714 and somewhat warmer brown dwarfs may indicate that they share a common origin for their variability (i.e., not water clouds). Based on these results and the previous work that we have discussed, we conclude that robust evidence of water clouds in the atmosphere of WISE 0855-0714 is not yet available.

We thank Caroline Morley and Mark Marley for providing their model calculations. We acknowledge support from a grant from NASA issued by the Jet Propulsion Laboratory (JPL), California Institute of Technology. The Spitzer Space Telescope is operated by JPL/Caltech under a contract with NASA. The Center for Exoplanets and Habitable Worlds is supported by the Pennsylvania State University, the Eberly College of Science, and the Pennsylvania Space Grant Consortium.